Surfacing undiagnosed disease: consideration, counting and coding

The diagnostic odyssey for people living with rare diseases (PLWRD) is often prolonged for myriad reasons including an initial failure to consider rare disease and challenges to systemically and systematically identifying and tracking undiagnosed diseases across the diagnostic journey. This often results in isolation, uncertainty, a delay to targeted treatments and increase in risk of complications with significant consequences for patient and family wellbeing. This article aims to highlight key time points to consider a rare disease diagnosis along with elements to consider in the potential operational classification for undiagnosed rare diseases during the diagnostic odyssey. We discuss the need to create a coding framework that traverses all stages of the diagnostic odyssey for PLWRD along with the potential benefits this will have to PLWRD and the wider community.

Performance of diagnostic coding and laboratory testing results to measure COVID-19 during pregnancy and associations with pregnancy outcomes

BACKGROUND

Large-scale evaluation of COVID-19 is likely to rely on the quality of ICD coding. However, little is known about the validity of ICD-coded COVID-19 diagnoses.

OBJECTIVES

To evaluate the performance of diagnostic codes in detecting COVID-19 during pregnancy.

METHODS

We used data from a national cohort of 78,283 individuals with a pregnancy ending between 11 March 2020 and 31 January 2021 in the OptumLabs^® Data Warehouse (OLDW). OLDW is a longitudinal, real-world data asset with de-identified administrative claims and electronic health record data. We identified all services with an ICD-10-CM diagnostic code of U07.1 and all laboratory claims records for COVID-19 diagnostic testing. We compared ICD-coded diagnoses to testing results to estimate positive and negative predictive values (PPV and NPV). To evaluate impact on risk estimation, we estimated risk of adverse pregnancy outcomes by source of exposure information.

RESULTS

Of 78,283 pregnancies, 5644 had a laboratory test result for COVID-19. Testing was most common among older individuals, Hispanic individuals, those with higher socioeconomic status and those with a diagnosed medical condition or pregnancy complication; 52% of COVID-19 cases was identified through ICD-coded diagnosis alone, 19% from laboratory test results alone and 29% from both sources. Agreement between ICD-coded diagnosis and laboratory testing records was high 91% (95% confidence interval [CI] 90, 92). However, the PPV of ICD-code diagnosis was low (36%; 95% CI 33, 39). We observed up to a 50% difference in risk estimates of adverse pregnancy outcomes when exposure was based on laboratory testing results or diagnostic coding alone.

CONCLUSIONS

More than one-in-five COVID-19 cases would be missed by using ICD-coded diagnoses alone to identify COVID-19 during pregnancy. Epidemiological studies exclusively relying on diagnostic coding or laboratory testing results are likely to be affected by exposure misclassification. Research and surveillance should draw upon multiple sources of COVID-19 diagnostic information.

Diagnostic Coding of Elder Mistreatment: Results From a National Database of Medicare Advantage and Private Insurance Patients, 2011-2017

Health care providers may play an important role in detection of elder mistreatment, which is common but underrecognized. We used the Health Care Cost Institute insurance claims database to describe elder mistreatment diagnosis among Medicare Advantage (MA) and private insurance patients in the United States from 2011 to 2017. We used International Classification of Diseases (ICD) coding to identify cases, examining the impact of transition from ICD-9 (Ninth Revision) to ICD-10 (Tenth Revision), which occurred in October 2015 and added 14 new codes for "suspected" mistreatment. 8,127 patients (0.051% of all aged ≥ 65), including 6,304 with MA (0.058%) and 1,823 with private insurance (0.026%) received elder mistreatment diagnosis. Transition from ICD-9 to ICD-10 was associated with a small increase in diagnosis rate, with "suspected" codes used in 45.3% of ICD-10 versus 9.7% of ICD-9 cases. Overall rates remained low. Rates, settings, and types of diagnosis differed between MA and private insurance patients.

Incidence and prevalence of psychogenic nonepileptic seizures in a Norwegian county: A 10-year population-based study

OBJECTIVE

This study was undertaken to measure the incidence and prevalence of active psychogenic nonepileptic seizures (PNES) in a Norwegian county.

METHODS

Using the Norwegian patient registry, we identified patients in Møre and Romsdal County in Norway diagnosed with F44.5 (conversion disorder with seizures or convulsions) or R56.8 (convulsions, not elsewhere classified) in the period January 2010 to January 2020. A review of the patients' medical records and an assessment of diagnostic validity were performed. PNES were diagnosed according to the recommendations by the International League Against Epilepsy Nonepileptic Seizures Task Force. Point prevalence of PNES on January 1, 2020 and incidence rates for the period 2010-2019 were determined.

RESULTS

Based on PNES within the past 5 years, we found a PNES prevalence of 23.8/100 000 (95% confidence interval [CI] = 17.9-29.6), including all levels of diagnostic certainty. For the highest level of diagnostic certainty (video-electroencephalographically confirmed), the prevalence was 10.6/100 000 (95% CI = 6.7-14.5). The highest prevalence was found in the age group 15-19 years, at 59.5/100 000 (95% CI = 22.6-96.3). The mean annual incidence rate between 2010 and 2019 was 3.1/100 000/year (95% CI = 2.4-3.7).

SIGNIFICANCE

We report for the first time a population-based estimate of the prevalence of PNES. Our findings suggest that the prevalence of PNES is within the range of estimates from non-population-based data. We found a strikingly high prevalence of PNES in the 15-19-year age group.

Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

Association of Diagnostic Coding-Based Frailty and Outcomes in Patients With Heart Failure: A Report From the Veterans Affairs Health System

Background

The aim of this study was to determine whether frailty is associated with increased admission and mortality risk in the setting of heart failure.

Methods and Results

This retrospective cohort analysis included patients treated within the Veterans Affairs Health System who had International Classification of Diseases, Ninth Revision (ICD‐9) codes for heart failure on 2 or more dates over a 2‐year period. The clinical variables identifiable in claims data, such as demographic variables and markers of physical and cognitive dysfunction, were used to identify patients meeting the frailty phenotype. Of 388 785 extracted patients with coding of heart failure between 2015 and 2018, 163 085 patients (41.9%) with ejection fraction (EF) measurement were included in the present analysis (38.3% with reduced EF and 61.7% with preserved EF). There were 16 660 patients (10.2%) who were identified as frail (9.1% in heart failure with reduced EF and 10.9% in heart failure with preserved EF). Frail patients were older, more often depressed, and were likely to have been admitted in the previous year. One‐year all‐cause mortality rate was 9.7% and 28.1%, and admission rate was 58.1% and 79.5% for nonfrail and frail patients, respectively. Frailty was associated with mortality and admission risk compared with the nonfrail group (adjusted odds ratio [OR], 1.71; 95% CI, 1.65–1.77 for mortality; adjusted OR, 1.29; 95% CI, 1.24–1.34 for admission) independent of EF.

Conclusions

Frailty based on diagnostic coding was associated with particularly higher risk of mortality despite adjustment for known clinical variables. Our findings underscore the importance of nontraditional parameters in the prognostic assessment.

Sensitivity of Administrative Coding in Identifying Inpatient Acute Strokes Complicating Procedures or Other Diseases in UK Hospitals

Background

Administrative hospital diagnostic coding data are increasingly used in “big data” research and to assess complication rates after surgery or acute medical conditions. Acute stroke is a common complication of several procedures/conditions, such as carotid interventions, but data are lacking on the sensitivity of administrative coding in identifying acute stroke during inpatient stay.

Methods and Results

Using all acute strokes ascertained in a population‐based cohort (2002–2017) as the reference, we determined the sensitivity of hospital administrative diagnostic codes (International Classification of Diseases, Tenth Revision; ICD‐10) for identifying acute strokes that occurred during hospital admission for other reasons, stratified by coding strategies, study periods, and stroke severity (National Institutes of Health Stroke Score</≥5). Of 3011 acute strokes, 198 (6.6%) occurred during hospital admissions for procedures/other diseases, including 122 (61.6%) major strokes. Using stroke‐specific codes (ICD‐10=I60–I61 and I63–I64) in the primary diagnostic position, 66 of the 198 cases were correctly identified (sensitivity for any stroke, 33.3%; 95% CI, 27.1–40.2; minor stroke, 30.3%; 95% CI, 21.0–41.5; major stroke, 35.2%; 95% CI, 27.2–44.2), with no improvement of sensitivity over time (P_trend=0.54). Sensitivity was lower during admissions for surgery/procedures than for other acute medical admissions (n/% 17/23.3% versus 49/39.2%; P=0.02). Sensitivity improved to 60.6% (53.6–67.2) for all and 61.6% (50.0–72.1) for surgery/procedures if other diagnostic positions were used, and to 65.2% (58.2–71.5) and 68.5% (56.9–78.1) respectively if combined with use of all possible nonspecific stroke‐related codes (ie, adding ICD‐10=I62 and I65–I68).

Conclusions

Low sensitivity of administrative coding in identifying acute strokes that occurred during admission does not support its use alone for audit of complication rates of procedures or hospitalization for other reasons.

Chronic kidney disease identification in a high-risk urban population: does automated eGFR reporting make a difference?

Whether automated estimated glomerular filtration rate (eGFR) reporting for patients is associated with improved provider recognition of chronic kidney disease (CKD), as measured by diagnostic coding of CKD in those with laboratory evidence of the disease, has not been explored in a poor, ethnically diverse, high-risk urban patient population. A retrospective cohort of 237 adult patients (≥ 20 years) with incident CKD (≥ 1 eGFR ≥ 60 ml/min/1.73 m(2), followed by ≥ 2 eGFRs <60 ml/min/1.73 m(2) ≥ 3 months apart)-pre- or post automated eGFR reporting-was identified within the San Francisco Department of Public Health Community Health Network (January 2005-July 2009). Patients were considered coded if any ICD-9-CM diagnostic codes for CKD (585.x), other kidney disease (580.x-581.x, 586.x), or diabetes (250.4) or hypertension (403.x, 404.x) CKD were present in the medical record within 6 months of incident CKD. Multivariable logistic regression was used to obtain adjusted odds ratios (ORs) for CKD coding. We found that, pre-eGFR reporting, 42.5 % of incident CKD patients were coded for CKD. Female gender, increased age, and non-Black race were associated with lower serum creatinine and lower prevalence of coding but comparable eGFR. Prevalence of coding was not statistically significantly higher overall (49.6 %, P = 0.27) or in subgroups after the institution of automated eGFR reporting. However, gaps in coding by age and gender were narrowed post-eGFR, even after adjustment for sociodemographic and clinical characteristics: 47.9 % of those <65 and 30.3 % of those ≥ 65 were coded pre-eGFR, compared to 49.0 % and 52.0 % post-eGFR (OR = 0.43 and 1.16); similarly, 53.2 % of males and 25.4 % of females were coded pre-eGFR compared to 52.8 % and 44.0 % post-eGFR (OR 0.28 vs. 0.64). Blacks were more likely to be coded in the post-eGFR period: OR = 1.08 and 1.43 (P (interaction) > 0.05). Automated eGFR reporting may help improve CKD recognition, but it is not sufficient to resolve under identification of CKD by safety net providers.

Administrative codes combined with medical records based criteria accurately identified bacterial infections among rheumatoid arthritis patients

OBJECTIVE

To evaluate diagnostic properties of International Classification of Diseases, Version 9 (ICD-9) diagnosis codes and infection criteria to identify bacterial infections among rheumatoid arthritis (RA) patients.

STUDY DESIGN AND SETTING

We performed a cross-sectional study of RA patients with and without ICD-9 codes for bacterial infections. Sixteen bacterial infection criteria were developed. Diagnostic properties of comprehensive and restrictive sets of ICD-9 codes and the infection criteria were tested against an adjudicated review of medical records.

RESULTS

Records on 162 RA patients with and 50 without purported bacterial infections were reviewed. Positive and negative predictive values of ICD-9 codes ranged from 54%-85% and 84%-100%, respectively. Positive predictive values of the medical records based criteria were 84% and 89% for "definite" and "definite or empirically treated" infections, respectively. Positive predictive value of infection criteria increased by 50% as disease prevalence increased using ICD-9 codes to enhance infection likelihood.

CONCLUSION

ICD-9 codes alone may misclassify bacterial infections in hospitalized RA patients. Misclassification varies with the specificity of the codes used and strength of evidence required to confirm infections. Combining ICD-9 codes with infection criteria identified infections with greatest accuracy. Novel infection criteria may limit the requirement to review medical records.